# Probability. The calculated likelihood that a given event will occur

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29-Mar-2015Category

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Probability Slide 2 The calculated likelihood that a given event will occur Slide 3 Methods of Determining Probability Empirical Experimental observation Example Process control Theoretical Uses known elements Example Coin toss, die rolling Subjective Assumptions Example I think that... Slide 4 Probability Components Experiment An activity with observable results Sample Space A set of all possible outcomes Event A subset of a sample space Outcome / Sample Point The result of an experiment Slide 5 Probability What is the probability of a tossed coin landing heads up? Probability Tree Experiment Sample Space Event Outcome Slide 6 Probability A way of communicating the belief that an event will occur. Expressed as a number between 0 and 1 fraction, percent, decimal, odds Total probability of all possible events totals 1 Slide 7 Relative Frequency The number of times an event will occur divided by the number of opportunities = Relative frequency of outcome x = Number of events with outcome x n = Total number of events Expressed as a number between 0 and 1 fraction, percent, decimal, odds Total frequency of all possible events totals 1 Slide 8 Probability What is the probability of a tossed coin landing heads up? How many possible outcomes? 2 How many desirable outcomes? 1 Probability Tree What is the probability of the coin landing tails up? Slide 9 Probability How many possible outcomes? How many desirable outcomes? 1 What is the probability of tossing a coin twice and it landing heads up both times? 4 HH HT TH TT Slide 10 Probability How many possible outcomes? How many desirable outcomes? 3 What is the probability of tossing a coin three times and it landing heads up exactly two times? 8 1 st 2 nd 3 rd HHH HHT HTH HTT THH THT TTH TTT Slide 11 Binomial Process Each trial has only two possible outcomes yes-no, on-off, right-wrong Trial outcomes are independent Tossing a coin does not affect future tosses Slide 12 Bernoulli Process P = Probability x = Number of times for a specific outcome within n trials n = Number of trials p = Probability of success on a single trial q = Probability of failure on a single trial ! = factorial product of all integers less than or equal Slide 13 Probability Distribution What is the probability of tossing a coin three times and it landing heads up two times? Slide 14 Law of Large Numbers Trial 1: Toss a single coin 5 times H,T,H,H,T P =.600 = 60% Trial 2: Toss a single coin 500 times H,H,H,T,T,H,T,T,T P =.502 = 50.2% Theoretical Probability =.5 = 50% The more trials that are conducted, the closer the results become to the theoretical probability Slide 15 Probability Independent events occurring simultaneously Product of individual probabilities If events A and B are independent, then the probability of A and B occurring is: P(A and B) = P AP B AND (Multiplication) Slide 16 Probability AND (Multiplication) What is the probability of rolling a 4 on a single die? How many possible outcomes? How many desirable outcomes? 1 6 What is the probability of rolling a 1 on a single die? How many possible outcomes? How many desirable outcomes? 1 6 What is the probability of rolling a 4 and then a 1 in sequential rolls? Slide 17 Probability Independent events occurring individually Sum of individual probabilities If events A and B are mutually exclusive, then the probability of A or B occurring is: P(A or B) = P A + P B OR (Addition) Slide 18 Probability OR (Addition) What is the probability of rolling a 4 on a single die? How many possible outcomes? How many desirable outcomes? 1 6 What is the probability of rolling a 1 on a single die? How many possible outcomes? How many desirable outcomes? 1 6 What is the probability of rolling a 4 or a 1 on a single die? Slide 19 Probability Independent event not occurring 1 minus the probability of occurrence P = 1 - P(A) NOT What is the probability of not rolling a 1 on a die? Slide 20 How many tens are in a deck? Probability Two cards are dealt from a shuffled deck. What is the probability that the first card is an ace and the second card is a face card or a ten? How many cards are in a deck? 52 4 12 4 How many aces are in a deck? How many face cards are in deck? Slide 21 Probability What is the probability that the first card is an ace? Since the first card was NOT a face, what is the probability that the second card is a face card? Since the first card was NOT a ten, what is the probability that the second card is a ten? Slide 22 Probability Two cards are dealt from a shuffled deck. What is the probability that the first card is an ace and the second card is a face card or a ten? If the first card is an ace, what is the probability that the second card is a face card or a ten? 31.37% Slide 23 Conditional Probability P(E|A) = Probability of event E, given A Example: One card is drawn from a shuffled deck. The probability it is a queen is P(queen) = However, if I already know it is face card P(queen | face)= Slide 24 Conditional Probability Probability of two events A and B both occurring = P(A and B) = P(A|B) P(B) = P(B|A) P(A) If A and B are independent, then P(A and B) = P(A) P(B) Slide 25 Bayes Theorem Calculates a conditional probability, based on all the ways the condition might have occurred. P( A | E ) = probability of A, given we already know the condition E = Slide 26 Bayes Theorem Example LCD screen components for a large cell phone manufacturing company are outsourced to three different vendors. Vendor A, B, and C supply 60%, 30%, and 10% of the required LCD screen components. Quality control experts have determined that.7% of vendor A, 1.4% of vendor B, and 1.9% of vendor C components are defective. If a cell phone was chosen at random and the LCD screen was determined to be defective, what is the probability that the LCD screen was produced by vendor A? Slide 27 Bayes Theorem Example Notation Used: P = Probability D = Defective A, B, and C denote vendors Unknown to be calculated: Probability the screen is from A, given that it is defective Slide 28 Bayes Theorem Example Known probabilities: Probability the screen is from A Probability the screen is from B Probability the screen is from C Slide 29 Bayes Theorem Example Probability the screen is defective given it is from C Probability the screen is defective given it is from A Probability the screen is defective given it is from B Known conditional probabilities: Slide 30 Bayes Theorem Example: Defective Part = P(screen is defective AND from A) P(screen is defective from anywhere) Slide 31 LCD Screen Example Slide 32 If a cell phone was chosen at random and the LCD screen was determined to be defective, what is the probability that the LCD screen was produced by vendor B? If a cell phone was chosen at random and the LCD screen was determined to be defective, what is the probability that the LCD screen was produced by vendor C?

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